{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pickle\n",
    "DATA_PATH = '../data/single_channel_nonlinear_mixing_tri_circ.pickle'\n",
    "with open(DATA_PATH, 'rb') as outfile:\n",
    "    data = pickle.load(outfile, encoding='latin1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " <metadata>\n",
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       "   <cc:Work>\n",
       "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
       "    <dc:date>2023-05-23T13:21:47.459653</dc:date>\n",
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       "    <dc:creator>\n",
       "     <cc:Agent>\n",
       "      <dc:title>Matplotlib v3.6.3, https://matplotlib.org/</dc:title>\n",
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      "text/plain": [
       "<Figure size 600x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%config InlineBackend.figure_formats = ['svg']\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from mpl_toolkits.axes_grid1 import ImageGrid\n",
    "fig = plt.figure(figsize=(6, 6))\n",
    "grid = ImageGrid(fig, 111,\n",
    "                 nrows_ncols=(1, 3),\n",
    "                 axes_pad=0.1,\n",
    "                 )\n",
    "\n",
    "idx = 0\n",
    "sample, circle, triangle = data[idx]\n",
    "\n",
    "labels = ['Mixed', 'Circle', 'Triangle']\n",
    "for ax, im, label in zip(grid, [sample, circle, triangle], labels):\n",
    "    ax.set_title(label)\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "    ax.imshow(im, cmap='gray')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ConvolutionalAutoencoder(\n",
       "  (encoders): ModuleList(\n",
       "    (0): ConvolutionalEncoder(\n",
       "      (encoder): Sequential(\n",
       "        (0): EncoderBlock(\n",
       "          (block): Sequential(\n",
       "            (0): Conv2d(1, 8, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "            (1): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "            (2): BatchNorm2d(8, eps=1e-05, momentum=0.8, affine=True, track_running_stats=True)\n",
       "            (3): Conv2d(8, 8, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "            (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "            (5): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "            (6): BatchNorm2d(8, eps=1e-05, momentum=0.8, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (1): EncoderBlock(\n",
       "          (block): Sequential(\n",
       "            (0): Conv2d(8, 16, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "            (1): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "            (2): BatchNorm2d(16, eps=1e-05, momentum=0.8, affine=True, track_running_stats=True)\n",
       "            (3): Conv2d(16, 16, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "            (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "            (5): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "            (6): BatchNorm2d(16, eps=1e-05, momentum=0.8, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (2): EncoderBlock(\n",
       "          (block): Sequential(\n",
       "            (0): Conv2d(16, 32, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "            (1): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "            (2): BatchNorm2d(32, eps=1e-05, momentum=0.8, affine=True, track_running_stats=True)\n",
       "            (3): Conv2d(32, 32, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "            (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "            (5): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "            (6): BatchNorm2d(32, eps=1e-05, momentum=0.8, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (3): EncoderBlock(\n",
       "          (block): Sequential(\n",
       "            (0): Conv2d(32, 48, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "            (1): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "            (2): BatchNorm2d(48, eps=1e-05, momentum=0.8, affine=True, track_running_stats=True)\n",
       "            (3): Conv2d(48, 48, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "            (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "            (5): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "            (6): BatchNorm2d(48, eps=1e-05, momentum=0.8, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (encoder_conv): Sequential(\n",
       "        (0): Conv2d(48, 32, kernel_size=(1, 1), stride=(1, 1))\n",
       "        (1): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "      )\n",
       "    )\n",
       "    (1): ConvolutionalEncoder(\n",
       "      (encoder): Sequential(\n",
       "        (0): EncoderBlock(\n",
       "          (block): Sequential(\n",
       "            (0): Conv2d(1, 8, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "            (1): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "            (2): BatchNorm2d(8, eps=1e-05, momentum=0.8, affine=True, track_running_stats=True)\n",
       "            (3): Conv2d(8, 8, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "            (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "            (5): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "            (6): BatchNorm2d(8, eps=1e-05, momentum=0.8, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (1): EncoderBlock(\n",
       "          (block): Sequential(\n",
       "            (0): Conv2d(8, 16, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "            (1): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "            (2): BatchNorm2d(16, eps=1e-05, momentum=0.8, affine=True, track_running_stats=True)\n",
       "            (3): Conv2d(16, 16, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "            (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "            (5): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "            (6): BatchNorm2d(16, eps=1e-05, momentum=0.8, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (2): EncoderBlock(\n",
       "          (block): Sequential(\n",
       "            (0): Conv2d(16, 32, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "            (1): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "            (2): BatchNorm2d(32, eps=1e-05, momentum=0.8, affine=True, track_running_stats=True)\n",
       "            (3): Conv2d(32, 32, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "            (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "            (5): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "            (6): BatchNorm2d(32, eps=1e-05, momentum=0.8, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (3): EncoderBlock(\n",
       "          (block): Sequential(\n",
       "            (0): Conv2d(32, 48, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "            (1): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "            (2): BatchNorm2d(48, eps=1e-05, momentum=0.8, affine=True, track_running_stats=True)\n",
       "            (3): Conv2d(48, 48, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "            (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "            (5): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "            (6): BatchNorm2d(48, eps=1e-05, momentum=0.8, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (encoder_conv): Sequential(\n",
       "        (0): Conv2d(48, 32, kernel_size=(1, 1), stride=(1, 1))\n",
       "        (1): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "      )\n",
       "    )\n",
       "    (2): ConvolutionalEncoder(\n",
       "      (encoder): Sequential(\n",
       "        (0): EncoderBlock(\n",
       "          (block): Sequential(\n",
       "            (0): Conv2d(1, 8, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "            (1): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "            (2): BatchNorm2d(8, eps=1e-05, momentum=0.8, affine=True, track_running_stats=True)\n",
       "            (3): Conv2d(8, 8, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "            (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "            (5): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "            (6): BatchNorm2d(8, eps=1e-05, momentum=0.8, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (1): EncoderBlock(\n",
       "          (block): Sequential(\n",
       "            (0): Conv2d(8, 16, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "            (1): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "            (2): BatchNorm2d(16, eps=1e-05, momentum=0.8, affine=True, track_running_stats=True)\n",
       "            (3): Conv2d(16, 16, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "            (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "            (5): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "            (6): BatchNorm2d(16, eps=1e-05, momentum=0.8, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (2): EncoderBlock(\n",
       "          (block): Sequential(\n",
       "            (0): Conv2d(16, 32, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "            (1): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "            (2): BatchNorm2d(32, eps=1e-05, momentum=0.8, affine=True, track_running_stats=True)\n",
       "            (3): Conv2d(32, 32, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "            (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "            (5): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "            (6): BatchNorm2d(32, eps=1e-05, momentum=0.8, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (3): EncoderBlock(\n",
       "          (block): Sequential(\n",
       "            (0): Conv2d(32, 48, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "            (1): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "            (2): BatchNorm2d(48, eps=1e-05, momentum=0.8, affine=True, track_running_stats=True)\n",
       "            (3): Conv2d(48, 48, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "            (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "            (5): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "            (6): BatchNorm2d(48, eps=1e-05, momentum=0.8, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (encoder_conv): Sequential(\n",
       "        (0): Conv2d(48, 32, kernel_size=(1, 1), stride=(1, 1))\n",
       "        (1): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (decoder): ConvolutionalDecoder(\n",
       "    (decoder): Sequential(\n",
       "      (0): Conv2d(96, 144, kernel_size=(1, 1), stride=(1, 1))\n",
       "      (1): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "      (2): GroupNorm(3, 144, eps=1e-05, affine=True)\n",
       "      (3): DecoderBlock(\n",
       "        (block): Sequential(\n",
       "          (0): Upsample(scale_factor=2.0, mode=nearest)\n",
       "          (1): ConvTranspose2d(144, 96, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "          (2): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "          (3): GroupNorm(3, 96, eps=1e-05, affine=True)\n",
       "        )\n",
       "      )\n",
       "      (4): DecoderBlock(\n",
       "        (block): Sequential(\n",
       "          (0): Upsample(scale_factor=2.0, mode=nearest)\n",
       "          (1): ConvTranspose2d(96, 48, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "          (2): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "          (3): GroupNorm(3, 48, eps=1e-05, affine=True)\n",
       "        )\n",
       "      )\n",
       "      (5): DecoderBlock(\n",
       "        (block): Sequential(\n",
       "          (0): Upsample(scale_factor=2.0, mode=nearest)\n",
       "          (1): ConvTranspose2d(48, 24, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "          (2): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "          (3): GroupNorm(3, 24, eps=1e-05, affine=True)\n",
       "        )\n",
       "      )\n",
       "      (6): DecoderBlock(\n",
       "        (block): Sequential(\n",
       "          (0): Upsample(scale_factor=2.0, mode=nearest)\n",
       "          (1): ConvTranspose2d(24, 24, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3))\n",
       "          (2): LeakyReLU(negative_slope=0.01, inplace=True)\n",
       "          (3): GroupNorm(3, 24, eps=1e-05, affine=True)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (output): Sequential(\n",
       "    (0): Conv2d(24, 1, kernel_size=(1, 1), stride=(1, 1))\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from omegaconf import OmegaConf\n",
    "\n",
    "import sys\n",
    "sys.path.append('..')\n",
    "from experiments.triangles_circles import Experiment\n",
    "\n",
    "# load model\n",
    "EXPR_PATH = '../checkpoints/tri_circ.ckpt'\n",
    "CONFIG_PATH = '../checkpoints/config.yaml'\n",
    "\n",
    "config = OmegaConf.load(CONFIG_PATH)\n",
    "experiment = Experiment(config.experiment_config)\n",
    "experiment = experiment.load_from_checkpoint(checkpoint_path=EXPR_PATH, config=config.experiment_config)\n",
    "\n",
    "model = experiment.model\n",
    "model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "<Figure size 600x600 with 16 Axes>"
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   ],
   "source": [
    "import torch\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "\n",
    "line_width = 0\n",
    "matplotlib.rcParams['axes.linewidth'] = line_width\n",
    "matplotlib.rcParams['ytick.major.width'] = line_width\n",
    "matplotlib.rcParams['xtick.major.width'] = line_width\n",
    "default_c = '#434343'\n",
    "matplotlib.rcParams.update({'axes.titlesize':15,\n",
    "                            'axes.labelsize': 16,\n",
    "                            'text.color' : f'{default_c}',\n",
    "                            'axes.labelcolor' : f'{default_c}'})\n",
    "\n",
    "def min_max(x):\n",
    "    return (x - np.min(x))/(np.max(x) - np.min(x))\n",
    "\n",
    "for idx in range(10):\n",
    "    sample, circle, triangle = data[idx]\n",
    "    sample, circle, triangle = min_max(sample), min_max(circle), min_max(triangle)\n",
    "\n",
    "    x = torch.tensor(sample, dtype=torch.float32).permute(2, 0, 1).unsqueeze_(0)\n",
    "    pred, _ = model(x)\n",
    "    pred = torch.sigmoid(pred).squeeze().unsqueeze(-1).detach().cpu().numpy()\n",
    "\n",
    "    with torch.no_grad():\n",
    "        z = model.encode(x)\n",
    "\n",
    "        z_a = [z[0], torch.zeros_like(z[1]), torch.zeros_like(z[0])]\n",
    "        z_b = [torch.zeros_like(z[0]), z[1], torch.zeros_like(z[0])]\n",
    "        z_c = [torch.zeros_like(z[0]), torch.zeros_like(z[0]), z[2]]\n",
    "        \n",
    "        y_a = model.decode(z_a)\n",
    "        y_b = model.decode(z_b)\n",
    "        y_c = model.decode(z_c)\n",
    "        \n",
    "    x_pred_a = torch.sigmoid(y_a).squeeze().unsqueeze(-1).detach().cpu().numpy()\n",
    "    x_pred_b = torch.sigmoid(y_b).squeeze().unsqueeze(-1).detach().cpu().numpy()\n",
    "    x_pred_c = torch.sigmoid(y_c).squeeze().unsqueeze(-1).detach().cpu().numpy()\n",
    "\n",
    "    %config InlineBackend.figure_formats = ['svg']\n",
    "    import matplotlib.pyplot as plt\n",
    "    %matplotlib inline\n",
    "    from mpl_toolkits.axes_grid1 import ImageGrid\n",
    "    fig = plt.figure(figsize=(6, 6))\n",
    "    grid = ImageGrid(fig, 111,\n",
    "                    nrows_ncols=(2, 4),\n",
    "                    axes_pad=0.15,\n",
    "                    )\n",
    "\n",
    "    labels = ['Mixed', 'Circle', 'Triangle']\n",
    "    images = [sample, circle, triangle, None, pred, x_pred_c, x_pred_a, x_pred_b] # order is switched for comparison reasons\n",
    "    y_labels = ['True', 'Pred.']\n",
    "    for i, (ax, im) in enumerate(zip(grid, images)):\n",
    "        if i != 3:\n",
    "            if i < len(labels):\n",
    "                ax.set_title(labels[i])\n",
    "            if i % 4 == 0:\n",
    "                ax.set_ylabel(y_labels[(i)//4])\n",
    "            if i+1 == len(images):\n",
    "                ax.set_title('(Dead Enc.)', color='gray', fontsize=12)\n",
    "            ax.set_xticks([])\n",
    "            ax.set_yticks([])\n",
    "            ax.imshow(im, cmap='gray')\n",
    "\n",
    "    plt.show()"
   ]
  }
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